## ----dependencies, include=FALSE----------------------------------------------
# Internal knitr settings
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

#Only execute the map if all dependencies are present
vignette_deps <- c("rnaturalearth", "rnaturalearthdata", "sf", 
                   "ggrepel", "dplyr", "ggspatial", "ggplot2")
all_present <- all(sapply(vignette_deps, requireNamespace, quietly = TRUE))
knitr::opts_chunk$set(eval = all_present)

## ----setup, message = FALSE, warning = FALSE----------------------------------
library(roroph)       
library(rnaturalearth) 
library(sf)
library(ggrepel)
library(dplyr)
library(ggspatial)
library(ggplot2)

# Load the dataset
data("roro_routes")

## ----map-network, fig.width=6, fig.height=10, fig.align='center', out.width="100%"----

# 1. Required Libraries
library(roroph)
library(rnaturalearth)
library(sf)
library(ggrepel)
library(dplyr)
library(ggspatial)

# 2. Data Preparation
ph_land <- ne_countries(scale = "medium", country = "Philippines", returnclass = "sf")

# Pre-calculate viz data once to avoid redundant grepl/mutates in the plot call
roro_viz <- roro_routes %>%
  mutate(
    highway_type = case_when(
      grepl("WNH", marina_code) ~ "Western",
      grepl("CNH", marina_code) ~ "Central",
      grepl("ENH", marina_code) ~ "Eastern",
      grepl("MR", marina_code)  ~ "Missionary",
      TRUE ~ "Other"
    ),
    highway_type = factor(highway_type, levels = c("Western", "Central", "Eastern", "Missionary"))
  )

# Optimized Hub Identification: Calculate distinct nodes and hubs in one pipeline
province_nodes <- roro_viz %>%
  group_by(from_prov) %>%
  summarise(
    total_freq = sum(freq_daily, na.rm = TRUE),
    lon = first(from_lon),
    lat = first(from_lat),
    .groups = "drop"
  )

# Identify Top 15 hubs + Batanes (Conditional logic inside slice to keep it pipe-friendly)
hubs_to_label <- province_nodes %>%
  arrange(desc(total_freq)) %>%
  slice(unique(c(1:15, which(from_prov == "Batanes"))))

# 3. High-Fidelity Plot Execution
ggplot() +
  # Layer 0: Global Background & Landmass
  geom_sf(data = ph_land, fill = "#ffffff", color = "#d1d8e0", linewidth = 0.3) +
  
  # Layer 1: RoRo Network Edges (Ordered by frequency so high-freq stays on top)
  geom_segment(data = roro_viz %>% arrange(freq_daily), 
               aes(x = from_lon, y = from_lat, 
                   xend = to_lon, yend = to_lat, 
                   color = highway_type,
                   linewidth = freq_daily), 
               alpha = 0.85, lineend = "round") +
  
  # Layer 2: Province Nodes (Using the pre-summarized province_nodes)
  geom_point(data = province_nodes, 
             aes(x = lon, y = lat), 
             color = "#2c3e50", size = 0.6) +
  
  # Layer 3: Strategic Labels (Enhanced repellent parameters)
  geom_text_repel(data = hubs_to_label, 
                  aes(x = lon, y = lat, label = from_prov),
                  size = 2.4, color = "#2f3542", fontface = "bold.italic",
                  box.padding = 0.8, point.padding = 0.5,
                  segment.color = '#bdc3c7', segment.alpha = 0.5,
                  max.overlaps = 20, force = 2) +
  
  # Professional Scales
  scale_linewidth_continuous(range = c(0.4, 2.5), breaks = c(5, 15, 30, 45), name = "TRIPS / DAY") +
  scale_color_manual(values = c("Western" = "#1e90ff", "Central" = "#2ed573", 
                                "Eastern" = "#ff7f50", "Missionary" = "#a4b0be"), 
                     name = "HIGHWAY SYSTEM") +
  
  # Cartographic Elements
  annotation_scale(location = "bl", width_hint = 0.15, text_size = 7) +
  annotation_north_arrow(location = "bl", which_north = "true", 
                         pad_x = unit(0.2, "in"), pad_y = unit(0.4, "in"),
                         style = north_arrow_minimal(text_size = 6)) +
  # --- Refined Theme & Unified HUD (West Philippine Sea Positioning) ---
  theme_minimal(base_family = "sans") +
  theme(
    plot.title    = element_text(face = "bold", size = 16, color = "#2c3e50", hjust = 0.5),
    plot.subtitle = element_text(size = 10, color = "#7f8c8d", hjust = 0.5, margin = margin(b = 10)),
    plot.caption  = element_text(size = 7, color = "#95a5a6", hjust = 0.5, margin = margin(t = 20)),
    
    # POSITIONING: Pinned to top-left sea area
    legend.position      = c(0.02, 0.98),
    legend.justification = c("left", "top"),
    legend.direction     = "vertical",
    legend.box           = "vertical",
    legend.box.just      = "left",
    
    # THE "EQUAL BOX" LOGIC
    legend.background = element_rect(fill = alpha("white", 0.8), color = "#bdc3c7", linewidth = 0.1),
    legend.margin     = margin(4, 6, 4, 6),
    legend.spacing.y  = unit(0.05, "cm"),
    
    # ULTRA-COMPACT SCALING
    legend.key.width  = unit(0.5, "cm"),   
    legend.key.height = unit(0.25, "cm"),
    legend.title      = element_text(size = 5.5, face = "bold"),
    legend.text       = element_text(size = 5),
    
    # CANVAS CLEANUP
    panel.grid      = element_blank(),
    axis.text       = element_blank(),
    axis.title      = element_blank(),
    panel.background = element_rect(fill = "#f1f2f6", color = NA),
    plot.background  = element_rect(fill = "#f1f2f6", color = NA)
  ) +
  
  labs(
    title    = "NATIONAL PHILIPPINE MARITIME ROUTE",
    subtitle = "Spatial Connectivity and Daily Frequency of the PH RoRo Network",
    caption  = "roroph package | Data: MARINA & PPA (2026) | Dev: NJ Talingting"
  ) +
  
  # PRECISE ARC MAP FRAMING
  coord_sf(xlim = c(116.5, 127.5), ylim = c(4.5, 21.5), expand = FALSE)

## ----analysis, warning=FALSE, fig.width=8, fig.height=5, out.width="100%"-----

# --- Analysis Plot with Integrated Professional Verdict ---
ggplot(roro_routes, aes(x = dist_nm, y = pax_cap, color = from_region)) +
  # Use jitter to reveal density at standard vessel classes (e.g., 300 pax)
  geom_jitter(size = 2.2, alpha = 0.5, width = 1.5, height = 5) +
  
  # LOESS capture regional non-linearities (The "Maritime Reality" Curve)
  geom_smooth(method = "loess", se = TRUE, alpha = 0.1, linewidth = 1) +
  
  # Regional Palette
  scale_color_manual(values = c("Luzon" = "#e74c3c", "Visayas" = "#3498db", "Mindanao" = "#2ecc71"), 
                     name = "Origin Region") +
  
  # Metadata and the Verdict
  labs(
    title = "REGIONAL MARITIME CAPACITY SCALING",
    subtitle = "Vessel Capacity (Pax) vs. Nautical Distance across the Philippine Archipelago",
    x = "Route Distance (Nautical Miles)",
    y = "Passenger Capacity per Trip",
    # YOUR VERDICT INTEGRATED HERE:
    caption = paste0(
      "Source: roroph 0.1.0 | dev: NJ Talingting |2026 Simulation Framework"
    )
  ) +
  
  # Professional Science Theme
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 14, color = "#2c3e50"),
    plot.subtitle = element_text(size = 10, color = "#7f8c8d", margin = margin(b = 15)),
    
    # Verdict Styling
    plot.caption = element_text(size = 8.5, color = "#34495e", hjust = 0, lineheight = 1.2, margin = margin(t = 20)),
    
    panel.grid.minor = element_blank(),
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    axis.title = element_text(face = "bold", size = 9)
  )